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by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds: probes each entity with ai_visibility_check, treats first entity as subject, returns ranked list with score/confidence/signal density. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (3-4 sentences), well-structured: operation, mechanism, use case, output format. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains the return format (ranked list with score, confidence, signal density) and covers all important aspects: input restrictions (2-8 entities), context usage, and the internal probing mechanism.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (baseline 3). The description adds that first entity is treated as 'subject' for narrative and rest as competitors, which provides semantic meaning beyond schema. Also explains models parameter usage briefly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool compares AI visibility across entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a concrete example and distinguishes from siblings by detailing its aggregation behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear use case ('competitive AI-marketing audits') and explains that it uses ai_visibility_check internally. However, it does not explicitly state when not to use this tool versus alternatives like single ai_visibility_check calls.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation3/5

Tools have distinct purposes but some overlap exists, e.g., multiple ask_pipeworx variants and deep_research could confuse an agent. Prediction market tools are differentiated but not immediately obvious.

Naming Consistency3/5

Names are consistently in snake_case but mix verb and noun orders (e.g., 'ai_visibility_check' vs 'ask_pipeworx'). No strict verb_noun pattern throughout.

Tool Count3/5

34 tools is on the high side but still reasonable given the broad domain coverage. Some tools could be consolidated without loss.

Completeness4/5

Covers data querying, company research, prediction markets, subscriptions, and memory. Minor gaps like no direct web search but ask_pipeworx substitutes.

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